Prediction method of city natural gas monthly consumption

doi:10.11949/j.issn.0438-1157.20150742

Abstract

Abstract:

A prediction model of city nature gas monthly consumption was established basing on city natural gas monthly consumption's limited growth and period of 12 months,the related parameters of different types of city of prediction model is analyzed,and example is applied.The results showed that the prediction model of city nature gas monthly consumption which was established basing on logistic and distribution curve,can be a good characterization of the growth of city gas in process characteristics and change rules; the characteristic value and its variation range of distribution curve was different from each type of city,and the width was different before and after the peak point curve.The peak and trough eigenvalue of distribution curve of natural gas monthly consumption was big difference when the city had central heating system,the peak and trough eigenvalue of distribution curve of natural gas monthly consumption was little difference when the city had non central heating system,the natural gas monthly consumption of complete production city had obvious individual characteristics.

Key words: gorithm, city, natural gas, consumption, model, prediction

摘要：

基于城市天然气用量具有"有限制增长"特点以及以12个月为周期变化的规律建立城市月用气量预测模型,分析不同类型城市预测模型的相关参数,并进行实例应用。结果表明,基于逻辑斯蒂原理以及月用气量变化分布曲线函数建立的城市月用气量预测模型可以很好地表征城市月用气量的增长过程特点和周期变化规律;不同类型城市的月用气量变化分布曲线特征值及其变化范围各异,而且高峰点前后曲线宽度不同。集中采暖类城市月用气量高峰特征值与低谷特征值相差较大,非集中采暖类城市月用气量高峰特征值与低谷特征值相近,完全生产类城市月用气量具有明显的个体特点。

关键词: 算法, 城市, 天然气, 用量, 模型, 预测

CLC Number:

TE89

LIANG Jinfeng, GUO Kaihua, HUANGFU Lixia. Prediction method of city natural gas monthly consumption[J]. CIESC Journal, 2015, 66(S2): 392-398.

梁金凤, 郭开华, 皇甫立霞. 城市天然气月用量的预测方法[J]. 化工学报, 2015, 66(S2): 392-398.

References 24

[1]	Mbamalu G A N,El-Hawary M E.Load forecasting via suboptimal season models and iteratively reweighted least squares estimation[J].IEEE Transations on Power Systems,1993,8(1):343-348.
[2]	David R Brillinger.Time Series Data Analysis and Theory[M].Berkeley:University of California at Berkeley,2001.
[3]	David Gottlieb,Steven A Orszag. Numerical Analysis of Spectral Methods:Theory and Applications(Sixth Printing)[M]. USA:the Society for Industrial and Applied Mathematics,1993.
[4]	Gorucu F B,Gumrah F.Evaluation and forecasting of gas consumption by statistical analysis[J].Applied Energy, 2004,26(3):267-276.
[5]	Sahbi Farhani,Muhammad Shahbaz,Mohamed Arouri,FrédéricTeulon.The role of natural gas consumption and trade in Tunisia's output[J].Energy Policy,2014,66:677-684.
[6]	Himadri Shekhar Dey,Md Ashfanoor Kabiry,Zia Wadudz,Shahidul Islam Khany,Md Abul Kalam Azad.Econometric modeling and forecasting of natural gas demand for power sector in Bangladesh//TENCON 2011-2011 IEEE Region 10 Conference[C]. 2011:1383-1386.
[7]	Sabo K,Scitovski R,Vazler I,et al.Mathematical models of natural gas consumption[J].Energy Conversion and Management, 2011,52(3):1721-1727.
[8]	Eugenio Fco Sánchez-AU' beda,Ana Berzosa.Modeling and forecasting industrial end-use natural gas consumption[J].Energy Economics,2007,29:710-742.
[9]	Marek Brabec,Ond Arěj Konár,Emil Pelikán,Marek Mal Ay'. A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers[J].International Journal of Forecasting,2008,24:659-678.
[10]	Marina Theodosiou.Disaggregation & aggregation of time series components:a hybrid forecasting approach using generalized regression neural networks and the theta method[J].Neurocomputing,2011,74:896-905.
[11]	Petr Musilek,Emil Pelikán,Tomáš Brabec,Milan Šim A?nek. Recurrent neural network based gating for natural gas load prediction system//International Joint Conference on Neural Networks[C]. Vancouver,Canada,2006:3736-3741.
[12]	Bo?idar Soldo,Primo? Poto A?nik,Goran Šimunovi A?Tomislav Šari A?Edvard Govekar.Improving the residential natural gas consumption forecasting models by using solar radiation[J].Energy and Buildings,2014,69:498-506.
[13]	Papalxopoulos A D,Hesterberg T C.A regression based approach to short term system load forcasting[J].IEEE Transactions on Power Systems,1990,5(4):1535-1547.
[14]	Pahasa J,Theera-Umpon N.Short-term load forecasting using wavelet transform and support vector machines[J].2007 Conference Proceedings Ipec,2007,(1-3):47-52.
[15]	Gorucu F B,Gumrah F.Evaluation and forecasting of gas consumption by statistical analysis[J].Energy Sources,2004,26(3):267-276.
[16]	Gutiérrez R,Nafidi A,Gutiérrez Sánchez R. Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model[J].Applied Energy,2005,80(2):115-124.
[17]	Vondracek J,Pelikan E,Konar O.A statistical model for the estimation of natural gas consumption[J].Applied Energy, 2008,85(5):362-370.
[18]	Martin Anthony.Discrete Mathematics of Neural Networks Selected Topics[M].Philadelphia:Society for Industrial and Applied Mathematics,2001.
[19]	The Intelligent Network:Current Technologies,Applications,and Operations[R].USA:International Engineering Consortium,1997.
[20]	Demirel O F,Zaim S,Caliskan A.Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods[J].Turkish Journal of Electrical Engineering and Computer Sciences,2012,20(5):695-711.
[21]	Kyung-shik S,Han I.Case-based reasoning supported by genetic algorithms for corporate bond rating[J].Expert Systems with Applications,1999,(16):85-95.
[22]	Fonseca C M,Fleming P J.An overview of evolutionary algorithms in multiobjective optimization[J].Evolutionary Computation,1995,3(1):165-180.
[23]	Nikolay A Kudryashov.Polynomials in logistic function and solitary waves of nonlinear differential equations[J]. Applied Mathematics and Computation,2013,219:9245-9253.
[24]	Humberto Ramos Quoirin,Kenichiro Umezu.The effects of indefinite nonlinear boundary conditions on the structure of the positive solutions set of a logistic equation[J].Differential Equations,2014,257:3935-3977.